class_docstr: Conditional Random Field layer (tf.keras)
  `CRF` can be used as the last layer in a network (as a classifier). Input shape (features)
  must be equal to the number of classes the CRF can predict (a linear layer is recommended).
  
  Note: the loss and accuracy functions of networks using `CRF` must
  use the provided loss and accuracy functions (denoted as loss and viterbi_accuracy)
  as the classification of sequences are used with the layers internal weights.
  
  Args:
      num_labels (int): the number of labels to tag each temporal input.
  
  Input shape:
      nD tensor with shape `(batch_size, sentence length, num_classes)`.
  
  Output shape:
      nD tensor with shape: `(batch_size, sentence length, num_classes)`.
code: def __init__(self, num_classes, **kwargs):
      self.transitions = None
      super(CRF, self).__init__(**kwargs)
      # num of output labels
      self.output_dim = int(num_classes)
      self.input_spec = tf.keras.layers.InputSpec(min_ndim=3)
      self.supports_masking = False
      self.sequence_lengths = None
  
file_path: nlp-architect/nlp_architect/nn/tensorflow/python/keras/layers/crf.py
imports: import tensorflow
label_desc: Use image node_id 1 to create a new CRF object from inherited base classes: tf.keras.layers with example: obj = CRF(num_classes)